Complex-Dynamic Fractality and Its Applications in Life Sciences
of the natural biological evolution dynamics, involving both relative permanence and
sudden “reasonable” change of species; (2) causally complete genetics taking into
account the whole picture of real genome interactions and thus providing the desirable
and reliable modifications; (3) unreduced understanding of the brain dynamics and
emergent, dynamic properties of intelligence and consciousness; (4) integral medicine
based on the causally complete understanding and creative control of each individual
organism dynamics; (5) genuine paradigm of nanotechnology based on the irreducibly
complex (multivalued) dynamics of nano-scale structures approaching them to the
natural, biological nano-machines; and (6) ecological and social applications of the
unreduced (multivalued) fractality and complexity characterised by the intrinsically
holistic analysis of the multi-level systems involved and providing provably efficient
solutions to the “global” problems (that cannot be solved within the unitary approach,
irrespective of the quantity of efforts [9]). Only such unreduced understanding of real
system dynamics can solve the growing “ethical” problems in practical research.
We shall consider here a more detailed outline of genetic applications, as they
become especially important because of the growing conceptually blind, but technically
powerful empirical experimentation with genomes of various organisms. The key result,
strongly supported by both experimental knowledge and the above theory, is that the
genome structure, operation, evolution, and related organism phenotype are mainly
determined by fractally structured genome interactions and not by sequential
“programme reading” a la Turing machine, as it is assumed by the current theory and
applications. Such understanding of genome dynamics is supported by the ensuing
unified solution to the well-known problem of “noncoding DNA”, relatively large in
quantity, but apparently “useless”, in the framework of unitary genetic paradigm. We
can see now that the existence of those relatively large DNA sections is necessary as
fractally structured gene interaction space and transmitter, similar to any real
interaction process and in agreement with experimentally observed correlation between
organism complexity and relative volume of those noncoding DNA parts [17].
As follows from sections 2 and 3, a unitary genetic programme cannot provide
“reasonable” development and would actually halt in any realistic operation mode. Its
efficiency is smaller than that of a real, dynamically multivalued, fractal interaction
process by a practically infinite quantity given by eq. (24). Unfortunately, this does not
exclude a possibility of purely empirical genome modification whose immediate
consequences, considered only within severely reduced unitary model, cover only a
negligibly small part of actually introduced change in the whole system dynamics,
remaining delayed in time and therefore “hidden” in mechanistic experimentation.
As has been shown in section 3, the huge dynamic complexity of brain or
genome operation is determined by the number of links between the system elements.
The number of synaptic links in human brain can be estimated as Nbrain = Nneuronnsyn ≈
1010 ×104 = 1014 , where Nneuron ≈ 1010 is the number of cells and nsyn ≈ 104 is the
number of links per cell. As follows from the universal symmetry of complexity
(section 2), the number of interaction links in the genome Ngenome, determining the
emerging brain complexity, cannot be smaller than Nbrain , Ngenome ≥ Nbrain . Since
Ngenome = Ngeneneff , where Ngene is the number of genes and neff is the number of
interaction links per gene, we have nβff ≥ Nbrain/Ngene ≈ 3 ×109 for human genome
( Ngene ≈3×104 ). It is remarkable that not only neff is quite large, supporting the key
role of gene interaction (both direct and indirect one), but in fact neff ≥ Nbase , where